The static bike rebalancing problem with optimal user incentives

The static bike rebalancing problem with optimal user incentives
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具有最佳用户激励的静态自行车再平衡问题

DOI:
10.1016/j.tre.2020.102216
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发表时间:
2021-02
期刊:
Transportation Research Part E: Logistics and Transportation Review
影响因子:
--
通讯作者:
Yang Liu
Yang Liu
中科院分区:
其他
文献类型:
--
作者:
Yanfeng Li;Yang Liu

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研究了具有最优用户激励的静态自行车再平衡问题。该问题被配制成一个混合整数非线性和非凸规划模型,以最小化总成本,包括旅行成本,不平衡的惩罚,和激励成本。我们重新建立了混合整数规划的形式,并发展了一种新的外逼近方法来获得其全局ε-最优解。我们还提出了一个双层可变邻域搜索算法来解决大型问题。小的例子上测试的结果揭示了问题的性质和性能的外近似方法。大规模算例测试结果表明,双层算法能以较短的计算时间提供高质量的解。
A static bike rebalancing problem with optimal user incentives is investigated. The problem is formulated as a mixed-integer nonlinear and nonconvex programming model to minimize the total cost, including the travel costs, unbalanced penalties, and incentive costs. We reformulate the mixed-integer program and develop a new outer-approximation method to obtain its global ε-optimal solutions. We also propose a bi-level variable neighborhood search algorithm to solve large problems. The results tested on small examples reveal problem properties and the performance of the outer-approximation method. The results tested on large examples show that the bi-level algorithm can provide high-quality solutions with short computational times.
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